Latest / The BioHub - by Avetix / Episode 219 - Prakash Raman - CEO of InduPro Therapeutics

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Episode 219 - Prakash Raman - CEO of InduPro Therapeutics · The BioHub - by Avetix

0:06
So welcome back to another episode of the Bio Hub, the
0:08
Avetics podcast. Today, we're thrilled to welcome
0:10
on Prakash Ramen, who's the CEO of Indipro Therapeutics.
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Prakash has spent more than two decades in biopharma, building a
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career that spans scientific research, drug development,
0:21
business development, licensing, and executive leadership.
0:24
Prakash has spent almost Prakash spent almost 14 years at
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Novartis, ultimately becoming VP and Global Head of business
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development and Licensing for the Novartis Institute for
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Biomedical Research before moving into venture creation
0:36
with Flagship. He later became President and
0:39
CEO of Rybon Therapeutics, giving him experience across big
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pharma, venture and biotech leadership.
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Today, he's applying that experience in the PRO, building
0:48
a company around a novel approach to proximity driven
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therapeutics. Today we're going to talk about
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Prakash's journey across the industry, the lessons learned,
0:57
the lessons he's learned moving between farmer venture and
1:00
biotech, and what attracted him to the science and the
1:03
opportunity in the PRO. So Prakash, look, it's an
1:07
absolute pleasure to have you on.
1:08
How are you? I'm doing great.
1:11
Thank you so much for the opportunity to chat with you
1:13
today. Yeah, it's it's a pleasure to
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have you on and just just as a beginning, just take us back
1:20
through your career, your journey through the field and
1:22
ultimately how you ended up where you are today.
1:25
So you've had spells at Novartis, Flagship and now in
1:28
the pro, as I mentioned. Can you just give us a high
1:30
level overview of your career today?
1:34
Absolutely. So as you probably mentioned,
1:37
I'm a scientist by training after my PhD in postdoc went in
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directly into industry first at Millennium and then moved to
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Novartis where I LED early stage research programs in a few
1:48
therapeutic areas. And then decided to transition
1:51
more to the business side. Because I did see it was an
1:54
opportunity for a trained scientist like me to actually
1:57
help evaluate external pipeline programs as they were being
2:01
evaluated for in licensing or other ways to partner with a
2:04
company like Novartis. So I did that and eventually
2:08
ended up leading that group as you mentioned.
2:11
So I not only did early stage discovery deals, but also LED
2:16
some late stage acquisitions as well as you know, really working
2:20
across different platform technologies, therapeutic areas
2:23
and then in licensing, you know, different assets and also out
2:26
licensing assets that Novartis was not going to move forward
2:30
internally. So it was a very fulfilling
2:32
career at Novartis, but then I decided to really turn to early
2:38
stage company creation because science has always been what
2:41
I've been excited about. Also I joined flagship, worked
2:44
across our portfolio of companies.
2:46
But then I realized that I really missed being an operator
2:50
because along the way at Novartis and other other
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companies that built skills around not only the scientific
2:55
leadership but also leadership in other functions that are key
3:00
for companies like finance, operations, etcetera.
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So it would be great to be an operator and lead a company,
3:05
which is where Ribon Therapeutics came in.
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And then when I got the opportunity to move to Indypro,
3:11
what really attracted to me and we'll talk about that in great
3:13
detail. It's just the underlying science
3:15
was so exciting and there was an opportunity to really be game
3:19
changing in the space of proximity biology and really
3:22
push novel therapeutics into the clinic and get get them to
3:27
patients. So I jumped at that opportunity
3:29
and I've been here over 2 years now and really enjoying the
3:32
right. Yeah, for sure.
3:35
Just just give us a high level overview for maybe someone that
3:38
that hasn't heard of Indipro for the first time or yeah, someone
3:41
that was new to the company. How would you describe the
3:45
company's thesis and what you guys are trying to do?
3:48
What Why do you believe the understanding of how proteins
3:50
are organised on the cell surface represents such a
3:53
fundamental shift in in how we discover new medicines?
3:56
Just just give us a high level overview of of what the
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company's actually trying to do. Absolutely.
4:01
As, as probably everyone knows, targeted therapies, especially
4:05
in areas like oncology have become the mainstay for
4:08
treatment for patients. And for that what people need to
4:11
understand is which targets are over expressed in tumor cells,
4:14
for example. And therefore we can selectively
4:16
go after them and hopefully be able to treat patients with
4:19
fewer side effects such as, you know, what is usually done with
4:23
cytotoxics. But unfortunately what's
4:25
happened is there's been a limit to the number of targets that
4:28
have been discovered that people can go after.
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And now in this current era, what people are trying to do is
4:35
actually pair targets together. So if two targets are Co
4:39
expressed in tumors, they're hoping that mate building
4:41
bispecifics for example, that bind to both would lead to
4:44
better activity than going after each individual target.
4:48
So that's the hypothesis. Unfortunately, that's not really
4:50
borne out. And we felt as, as our science
4:53
was getting under way, that understanding how those protein
4:56
targets are displayed on the cell surface actually plays a
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very important role in actually predicting which pairings are
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likely to have that enhanced biology that we'd go after by
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building the appropriate bispecifics.
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But in order to do that, the technology really had to evolve.
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And we fortunately have been at the forefront of that, pushing
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that science forward. And it's really, really exciting
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because now we have high resolution maps of what's going
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on in the cell surface and tumor cells.
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Compare the those neighborhoods and tumor cells with the
5:28
corresponding normal cells to see if there's some differential
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Co expression differential clustering of those proteins in
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the same space in tumor cells so that we can actually then so
5:38
more even more selectively go after those tumor cells.
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So that was the hypothesis. And over the four years of the
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company has been in existence that we have really built the
5:47
tool kit internally to have very high resolution maps across
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multiple known anchor tumor antigens as we have called them
5:55
and built and actually predicted now many, many new first in
6:00
class pairings for those tumor antigens.
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And that's really expanded the target space significantly.
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And that's that's where we felt there was an opportunity to
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really now leverage that platform to not only identify
6:12
which protein targets are potentially Co expressing tumor
6:16
cells and we can differentially go after them, but also build
6:19
novel therapeutics to be able to then give to patients who have
6:23
those particular tumors as well as those Co expression of those
6:27
tumor targets. So that was the hypothesis.
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And then as you can see even on our website, we have now a very
6:33
robust pipeline of how that understanding the proximity of
6:37
those proteins on the cell surface has LED us to predict
6:40
pairings that are likely to have that enhanced shared biology.
6:44
And now is left to it led to a robust first in class pipeline
6:48
of therapeutics that we're moving forward.
6:50
And it's actually a great day that you caught me because today
6:53
we just announced our first program is transitioned to the
6:56
clinic and we have dosed our first patient.
6:58
So that's really our the first translation of that overlying
7:01
science into actually a therapeutic that's now been
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dosed to a patient. Yeah, amazing news.
7:07
And just so you've obviously had a very successful career.
7:12
You've spent a lot of time in pharma venture, you've held of
7:16
the CEO positions before. What what was it specifically
7:18
about in the Pro that attracted you?
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I would imagine that you had various different options that
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you were looking at. I'm sure this probably wasn't
7:25
the only company, but what specifically about in the Pro
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resonated you with you that made you think that this was the
7:32
right opportunity for you to to come in and and take the company
7:35
forward? Now I was, I was very fortunate
7:38
that I did have other opportunities, but what really
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attracted me to Indipro was the fact that the underlying science
7:44
was very different from what I always saw that was out there in
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the biologic space. I knew that the ADC space,
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that's the antibody drug conjugate space as well as multi
7:53
specifics such as bispecifics were becoming very trendy.
7:57
But there was really no, as far as I could see, national
8:01
approach with which to predict pairing so that you can actually
8:04
build the appropriate therapeutics.
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And Interpro is the first one that I saw that had a very
8:09
foundational science that would be able to predict those
8:13
pairings. And this particular technology,
8:15
which is a photochemistry that was invented by our founders has
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really evolved to a point where we, as I said earlier, we have
8:23
very high resolution maps of what's going on in the cell
8:26
surface. And what is even more important
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to me was not only the prediction of which pairs, but
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also the fact that the company had built the in house
8:34
capabilities to then translate those pairings to actual
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molecules. As you probably know, building a
8:40
bispecific molecule is not just knowing 2 targets and taking
8:44
binders to those and slapping them together in a format.
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It's really you need the right epitope that you have to go
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after, you need the right geometry, you need the right
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tuning and binding affinity. And all those capabilities were
8:56
built in house along with the foundational proximity mapping,
8:59
which I felt was going to be a differentiator for other
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companies that claim they can do the same thing.
9:04
Because I think pairing both the underlying science for
9:08
predicting pairings as well as then the biologics capability to
9:12
build the right therapeutics would actually then function as
9:15
a really great company to build a pipeline with.
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Because that translation, not everyone would be able to do.
9:21
So if I wanted to go and work for a company that had that
9:25
unique capability that others would have a harder time
9:28
replicating. But more importantly, I wanted
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to work on innovative, innovative products.
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I didn't want to work on you know more, you know, me betters
9:36
or any other other such approaches in the biologic
9:39
space. I wanted to really truly be
9:41
innovative products and finding novel targets and target
9:44
pairings. I think alongside being able to
9:47
build the right therapeutic would be the the game changer
9:51
that the patients are are waiting for.
9:53
So that's why I decided to join in the pro.
9:57
Nice, amazing. One of the things I know we
10:00
we've spoken about before, but the the word platform is a word
10:05
that's become very overused, a very overused term.
10:09
Invest investors become increasingly sceptical of
10:11
companies that claim to be a platform business.
10:15
What when you like actually describing the pro as a platform
10:18
company? What?
10:19
What does that actually mean in practice and what
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characteristics separate a true platform company from a
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collection of technologies or that pipeline with good
10:27
marketing? Yeah, that's a great question.
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And I don't want to disrespect many of the platform companies
10:34
that are out there. But in my mind, what a true
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platform is, is really just the basis that on which you can
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actually build novel assets and novel therapeutics on.
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So the platform, the way I see it at Indipro is it forms a
10:48
basis for us, as I said earlier, to identify pairings that are
10:52
likely to have better activity in biology compared to going
10:56
after each individual target antigen.
10:58
But the key capability then is to to pick the right pairings
11:02
and then identify where exactly we can place that particular
11:06
bispecific pairing in the context of the disease
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indication of choice where there's significant unmet need
11:12
as well as be then being able to build the appropriate first in
11:14
class bispecific therapeutic that we can then interrogate and
11:18
and move into the clinic and and show hopefully a difference for
11:21
patients. So the platform is just the
11:23
foundation, but in my mind, what's really transfer, at least
11:27
transformational, the way we are using the platform is we're
11:30
actually focused on which assets are going to come out of those
11:33
platforms that can actually make a difference for patients.
11:36
And that's where I think, you know, having such a broad
11:39
platform where there's multiple different opportunities, but
11:42
then being able to then select which ones we would actually go
11:45
after and things that we can do ourselves, then demonstrate that
11:48
translation to the clinic was what I think really
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distinguishes into Pro from a lot of other platform companies.
11:55
Platform companies exist because that's a way to de risk if, if
11:59
the first attempt that comes out of that platform fails, is still
12:02
an opportunity to go back and identify another one and then
12:06
move that forward. So I think that's the main
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reason why people have built a lot of platform tech platform
12:11
companies. But in my mind, platform, the
12:14
way we're using it is very different.
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We're using the platform just as a foundation and building on it
12:19
to really build a pipeline of differentiated first in class
12:22
therapeutics. Yeah, it's over the past year,
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you guys have definitely had some very significant milestones
12:30
for the company. I know you mentioned that you do
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see first patient today, congratulations.
12:35
You just had a very impressive Series A around 85,000,000.
12:39
Then today also announced Series B Congratulations on that 77
12:43
million, which is amazing as well as major pharmaceutical
12:46
partnerships as well. Just talk us through like, what
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do you think those milestones tell you about how the industry
12:52
is beginning to view proximity biology and what kinds of
12:56
external validation were most meaningful to you as a
12:59
leadership team with with those milestones?
13:02
That's a great question. As I said, you really caught us
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on a great day because you're right, we did announce our
13:07
Series B alongside the first in human dosing.
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So we are very fortunate to have a group of investors who really
13:14
believe in our science and we've been able to attract additional
13:17
ones along the way as we erase both our Series A and Series B.
13:21
The, the way I look at it is, I think what attracted the our
13:25
investors to the sciences that they wanted to really help build
13:29
a transformational platform as we just discussed.
13:32
But more importantly, they wanted to make sure that that
13:35
would then allow us to build innovative products as well,
13:38
because at the end of the day, you want a therapeutic, an
13:42
actual medicine for patients as opposed to just saying, I have a
13:44
cool platform. And so they saw that link
13:47
between what that original platform science was to where we
13:52
could actually then build the novel therapeutics that could
13:56
then move on to the clinic and benefit patients.
13:58
So for that I'm very grateful for our our investors.
14:01
But more importantly, as you also mentioned along the way, as
14:05
we were building out the platform, the science and then
14:09
our initial pipeline as well, we actually had a lot of
14:14
discussions with strategic partners and other pharma
14:16
companies. And it was very reassuring to us
14:20
as we started having those discussions were two things.
14:23
One is they were very excited that you know we're pushing the
14:25
boundary of where proximity biology was and we were really
14:28
innovating on this the approach of this photo, photo labeling
14:33
which was non perturbing, which is again very different from the
14:35
other technical that were present out there.
14:37
So they really felt with a very good handle on how exactly would
14:41
go about identifying those target pairs.
14:44
But secondly, it was very consistent that in almost every
14:48
discussion that we had with a pharma partner, they had some
14:51
form of proximity biology exploration within their own R&D
14:56
teams. So it really felt reassuring to
14:59
us that, you know, these pharma partners, they understood what
15:02
we're trying to do. But more importantly, you know,
15:04
they also saw where we were potentially different from what
15:08
how they were approaching things as well.
15:10
And at least, you know, two of those discussions now have gone
15:13
on to become our core collaborations, one with Sanofi
15:17
around an autoimmune program where we identified a novel
15:21
pairing, showed preclinical data, they got really excited
15:24
and and wanted to partner with us on that.
15:26
And then the other collaboration that we announced earlier this
15:29
year with Lily was around novel targets of interest to them
15:33
where we would be helping them identify novel pairings using
15:37
our platform and then build appropriate therapeutics based
15:40
on those particular pairings that we not only identify but
15:43
also help prioritize. So it was a great way for us to
15:46
continue to build on our platform capability working with
15:50
these pharma partners because at the end of the day, they have
15:53
the vision of, you know where they would like these particular
15:56
assets to sit within their broader pipeline.
15:59
And so using that and then backtracking to exactly which
16:03
pairings or which bispecifics would actually help build the
16:07
appropriate therapeutics for those indications of interest to
16:10
them was was something that we felt we we could learn from and
16:14
they would benefit from how our platform would then be able to
16:16
prioritize and hopefully identify really novel pairings
16:20
that we can then build the therapeutics around.
16:22
So I'm really happy to say that those collaborations are going
16:26
well. And we continue to have lots of
16:29
discussions with other pharma companies, not only on the
16:32
platform itself, but also potentially some of the assets
16:35
that we're moving forward. So stay tuned.
16:37
You know, I'm sure there'll be more news to come in in the near
16:40
future. Yeah, amazing.
16:43
An amazing set of investors that you guys had as well.
16:46
So congratulations again on that.
16:49
Another topic that I think is, is, is I guess on the top of
16:53
everyone's lips at the moment is AIA bit of a buzzword in the
16:57
industry at the moment. A lot of companies are if they
17:00
weren't already using it in some way.
17:03
I think a lot of companies are kind of still working from this,
17:06
the same publicly publicly available data sets in the Pro,
17:09
you've built your own proprietary biological data
17:13
sets. How do you think about AI?
17:15
How are you guys seeing AI as a company, not just as a buzzword,
17:18
but as a genuine kind of competitive advantage over
17:22
maybe, maybe some of the others that are in the same field as
17:25
you guys? There's definitely a lot of hope
17:29
and hype around AI, especially in drug discovery and
17:31
development. What we've done is actually
17:34
embraced the parts that actually made sense for us to use with
17:38
our platform. And by that I mean, if you use
17:41
the same AI tools or you build your own AI tools to introgate
17:46
public data sets, it basically is very dependent on the quality
17:49
of the data that you're actually using those algorithms to train
17:53
on. What we felt was very important
17:55
was that we actually build our own proprietary database with
17:59
very, with very high quality data that we generate using our
18:02
proximity labeling platform that I described earlier.
18:05
And that data set is, is, you know, curated.
18:08
It's the qualities checked and, and we have not only the
18:12
proximity maps, but we also do a lot of cell surface protein
18:17
quantification because we not only want to understand which
18:20
proteins are proximal, we also know want to know how much of
18:23
those proteins are actually expressed on the cell surface.
18:26
Because as you know, gene expression is not necessarily
18:28
always equal to protein expression on the cell surface.
18:31
So those kind of proprietary data sets are key.
18:34
And the quality that we have been able to do the math is also
18:38
very important for us then to be able to leverage the AI tools
18:41
that we built to help us parse through that data set.
18:45
And we also incorporate additional public data sets as
18:48
well because there is data known on targets like the pathways,
18:52
common mechanisms, etc, signaling mechanisms.
18:55
So those are all also input into our database along with our
18:59
proprietary data sets. And that's where we really need
19:03
AI to be able to look at that multi dimensional data and then
19:06
make it much more, at least package it in a way that makes
19:11
it much more meaningful for us to be then be able to make
19:14
predictions on which parents are likely to have that enhanced
19:18
biology that it's spoken about earlier.
19:21
So I think the AI tools that we have built are really tailor
19:25
made for what we need. But it's, it's something that I
19:30
think is very dependent on the quality of data that goes in.
19:33
There's definitely, you know, every company, there's a lot of
19:37
companies out there, I should say, that are using AI now to
19:41
help accelerate their drug discovery and development
19:44
efforts. But the data that they use is
19:47
then you know, their actual limitation as well, because that
19:50
quality, the amount of data that you have is going to be very
19:54
important for them those their AI tools to be able to predict
19:57
what they want them, what they want to be able to do with their
20:00
particular data set, right. So I think that's where we are
20:04
kind of different. We we really focus on, we built
20:07
a. A very unique tools kit with AI
20:11
to be able to interrogate our database to be able to predict
20:14
those pairings. So that that's kind of the
20:16
difference. But having said that, you know,
20:18
I think AI is definitely helping us as well accelerate some of
20:22
those efforts because as I said, the multi dimensional data sets
20:26
is very hard to parse through all that without using tools
20:29
like that. And and I think that's been very
20:30
important for us in our workflow.
20:34
Yeah. Another topic that that everyone
20:35
has kind of been speaking about at the moment is China, not just
20:39
in terms of the innovation that's coming out of there, but
20:41
also it's been, you know, one of the world's leading centres for
20:44
antibody engineering and biologics development with
20:48
companies capable of moving incredibly quickly and
20:51
efficiently. How has that changed the
20:54
competitive landscape for biotech companies in the US?
20:56
And where do you believe companies like Indipro can truly
21:01
benefit and take advantage of of what's moving out from there?
21:05
Yeah, that's a great question. That's a very timely question
21:08
because we, you know, discussed that almost on a daily basis
21:13
internally as well. I think the the key thing to
21:15
understand is that China has been building these capabilities
21:20
and the the fact that they've reached a point where they can
21:23
actually swiftly take anything that's let's say known among a
21:26
particular target or target and then build the appropriate
21:29
biologics as well as not only the the bispecific for multi
21:34
specifics, but also in different ADC payloads etcetera.
21:38
And more importantly take those findings to then build the
21:42
appropriate therapeutics and then quickly start generating
21:46
clinical perfect concept data. I think that's kind of
21:48
unparalleled. It's going to be very difficult
21:50
for us to do that here in the US or anywhere outside of China.
21:56
So I think what we have done is actually lean into sort of what
22:00
we think that that's right for us.
22:03
The key I think that differentiates our platform is
22:05
that understanding of which target pairings are likely to
22:08
make a difference. So that's something that's
22:10
proprietary to us and that's innovative, which I think again,
22:15
it's as far as we know has not really come up to that same
22:18
stage of optimization in China. So I think that's that's our
22:22
edge here. But then that also means that we
22:25
need to be then be lean and focused in our execution because
22:29
it's all about translating those findings into building their
22:34
therapeutics and then of course translating that into a human
22:37
clinical data. So we're laser focused on our
22:40
lead program now because I think it came out of our platform.
22:44
We've quickly now transitioned that into a very innovative
22:49
first in class molecule and that molecule now is impatient.
22:52
So I think that's where, you know, for a company our size to
22:56
be able to be very focused on our execution and really focus
23:02
morally more on the innovative aspects of our portfolio, I
23:07
think that that that's where we can differentiate.
23:09
Otherwise, you know, it becomes a very difficult chase that we
23:15
have with the Chinese companies. If they're working on the same
23:17
things, it'll be very difficult, given the challenges we have
23:20
outside of China, to be able to quickly not only build the right
23:24
molecules but also develop them with all the regulatory
23:27
constraints that we have. Yeah, for sure.
23:32
I think one of the things that that probably every biotech
23:35
company, every biotech CEO faces the challenge of is, is scaling
23:39
an organization was trying to remain disciplined with with
23:43
capital and funds. Like from your perspective, how
23:46
do you decide when it's worth building capabilities internally
23:49
versus leveraging partners? And what principles guide those
23:53
decisions as you think about extending runway without slowing
23:57
down innovation? Just talk us through that
23:59
because I think it's really, really interesting.
24:01
It's a it's a question I have to ask myself everyday because at
24:06
the end of the day, it's all about speed to data and time.
24:11
Time is the most important piece that we can ever get back.
24:14
So what we typically do is when we look at a workflow, we really
24:18
ask ourselves a question, is this something that, you know,
24:21
we can do better ourselves internally or is it worthwhile
24:26
just getting it done as CRO because they can do it much
24:29
faster at a scale that we are not capable of with our
24:32
facilities, for example. So that's where we, we ask that
24:35
question of like almost every item in our workflow.
24:38
And we have optimized it such that we, we realized very early
24:41
on that the database that we have, we have to do it in house
24:45
because no one else has those capabilities in CRO.
24:48
But once we get it to the point where, you know, we can actually
24:52
have predicted target pairs, we also found that if we have our
24:56
own biologics team internally, we can actually do a lot of the
25:00
screening and the detuning and the final format selection much,
25:05
much faster in house than if we were to use a general CRO to be
25:09
able to do that. Where the CRO's really come in
25:11
handy is when we want to scale. So if we want to start
25:15
production runs, if we want to generate more material to be
25:18
able to do you know, those in vivo studies, ID enabling
25:21
studies and of course the clinical material.
25:23
That's not something that makes sense for us to build
25:25
capabilities for in house. So I think we've reached this
25:29
happy medium where we do a lot of the initial work to identify
25:34
select molecule. But then in terms of the
25:36
production, we really rely a lot on our CRO partners to be able
25:41
to take that forward. And the same thing with the
25:43
clinical side as well. We do have a small clinical
25:46
team, but as you know, if we're running, you know trials across
25:49
multiple sites, you do need a lot of help and support.
25:53
That's where clinical CRO also becomes very important for us
25:56
because it doesn't make sense for us to build all the
25:59
capabilities in house that CRO already has to, to take a
26:03
program into the clinic, especially a first in human dose
26:06
escalation, for example. They, they have they've done
26:09
that many times before. They have the capabilities in
26:12
house. So it made sense for us to
26:13
leverage that. So I think we're very, we have
26:17
tried to optimize where we can do things better internally
26:22
versus where CRO's would actually be better at doing it
26:24
then. But it's a question we ask every
26:27
time there is in addition to the workflow, because now what we
26:31
don't want to do is lose time. Yeah, for sure.
26:36
As you look ahead over the next few years, Indypro, what are
26:39
you, what do you see is like the next critical inflection points
26:43
for the company? I imagine after the fundraising
26:47
round you just announced, there's going to be a lot on
26:48
execution now, but just even longer term, the next three to
26:51
five years, just talk us through like what you guys are hoping to
26:54
achieve and which milestones would tell you that the platform
26:57
has truly crossed into a new phase of maturity as well.
27:02
Just talking about what you guys are going to be excited about.
27:04
Coming up, I think the first key milestone for us will be that
27:08
early proof concept data for our IDP 001, our lead program that
27:13
just dosed the first patient. We're hoping to see some initial
27:17
clinical data by the end of 27 early 28.
27:21
So I think that not that not only would validate the program,
27:25
but more importantly also validate the platform because
27:28
the selection of the targets was really based on our platform
27:31
just predicted that this pairing would be better than going after
27:34
each tumor antigen alone and also potentially have better
27:38
tolerability based on some of the tolerability issues that are
27:42
known with EGFR therapies. So I think the validation of the
27:45
platform plus of course the validation of the drug itself is
27:49
going to be the key milestone we're looking for.
27:52
It's the end of 27, early 28, but of course you know we're not
27:56
staying put. We have additional programs that
27:59
are moving through our funnel and preclinical development and
28:04
we hope to have at least one additional molecule that could
28:07
enter the clinic sometime in 2027.
28:10
But then beyond that, we not only have the pipeline that's
28:14
some of it is partnered, but also some work that's also going
28:18
on with our platform internally. We're hoping to also start
28:21
moving those forward either by ourselves or you know, in the
28:25
context of additional partnerships that we hope to put
28:27
in place as well. So I think there's significant
28:30
opportunity even beyond just the lead program.
28:33
But again, the the lead program is is going to be the key
28:35
milestone. But we're hoping to become a
28:38
company where we have a self-sustaining pipeline where
28:40
we, you know, take our early discoveries, be able to then
28:44
generate the translation into the clinical proof of concept
28:49
data and then of course figure out what happens after that.
28:53
Either we move those molecules forward ourselves into
28:56
development by raising the appropriate funds or find
28:59
partners because at the end of the day, some of these later
29:02
stage pivotal studies are are very expensive and they also
29:06
need global reach. So it's a, it's something that
29:09
we think that might be better to do with a partner, but then
29:12
we'll decide based on what data we actually start seeing in the
29:15
clinic. Yeah, for sure.
29:19
One thing I wanted to talk about with you as well was leadership.
29:21
You've obviously, Yeah, you've LED highly accomplished teams in
29:26
large farmer, in biotech in in venture.
29:31
Do you think your leadership style has evolved or changed at
29:33
all over time? I guess just being in different
29:36
environments. And what do you think you've
29:37
learned about creating an environment where exceptional
29:40
scientists feel empowered to challenge assumptions, take
29:43
calculated risk, and pursue bold ideas?
29:47
You took us through like, has it changed at all?
29:50
Do you think you you're the same leader as you were 10 years ago?
29:54
No, I'm definitely not. I've definitely changed over the
29:59
course of my career. Initially as a starting, you
30:02
know, project team lead. That was my first kind of
30:04
leadership role. It was a very different leader
30:08
that I I think I was at the time because it was all about making
30:12
sure that the team was focused, but really driving a lot of
30:16
different aspects of a project. Where I find myself now as
30:20
leading this company is every leader at the end of the day is
30:26
built a team that they're very happy to work with.
30:29
What we've been able to do it in the Pro is really find great
30:31
leaders for each individual function that we have.
30:35
And I think my job is actually to get out of the way and let
30:38
them do what they're really good at and their function, but be a
30:41
good listener. Because at the end, you, what
30:44
you want is for them to be able to raise concerns, raise issues
30:48
that they are working on. And then with your experience
30:51
and hopefully the, you know, talking, talking about it with
30:54
them, you can actually come up with solutions that could
30:57
address those issues and then hopefully solve them.
31:00
So I think my job has become more of a sort of a leader who
31:05
is really there for their people, but get out of their way
31:08
and let them do their job, but really help them focus and
31:13
execute on what they need to do and make their lives easier so
31:18
that they can actually do that. So I think that that's how my
31:21
leadership now so has evolved. I think I've become much more of
31:24
a listener now than maybe I used to be.
31:28
But but also it's important that, you know, I keep, I keep
31:31
tabs on, you know, what everyone is doing, but in a way that, you
31:35
know, is really more supportive and helpful as opposed to
31:38
micromanaging. Yeah, and that this is a
31:41
question that we always finish with as well.
31:44
If you were to sit down, have a dinner with any free people from
31:47
past or present, but who would you pick and why?
31:50
It doesn't necessarily need to be people in the industry.
31:52
It can be sports people, it can be historians, musicians,
31:56
whoever you like. But if you'd sit down and have a
31:58
dinner with any free people, who would it be?
32:01
And I guess, I guess why would it be them?
32:03
And maybe what would you want to?
32:04
What would you want to learn from them?
32:07
Yeah, I know that's a, it's a great question.
32:09
I gave it a little bit of thought.
32:11
Not surprisingly, I, I think I mainly chose all scientists.
32:15
Sorry about that. The The, the first person that
32:19
I, that I really, I think owe a lot to is Emil Fisher.
32:23
I don't know if you know him, but he's the first person who
32:26
came up with this whole lock and key model.
32:28
I remember in my undergrad at my first course in medicinal
32:32
chemistry where, you know, they're talking about aspartic
32:35
proteases and how you have the ability to build a molecule that
32:39
can actually fit into an enzyme pocket, block its function, and
32:43
therefore, you know, change the outcome for a disease, right?
32:46
And that to me was amazing that, you know, human could actually
32:49
create molecules that can, you know, you know, interrupt
32:52
functions of proteins and then be able to build appropriate
32:56
medicines from that. So I think that that to me has
33:00
been the guiding way that so I, you know, trained in medicinal
33:04
chemistry, I worked in the industry.
33:06
It just it, this is something where the simple concept of
33:09
being able to interrupt proteins and interrupt their function has
33:12
led to, you know, so many novel medicines that have come out.
33:16
So that to me is inspiring and definitely would love an
33:19
opportunity to to meet with them because that that was not a
33:23
dogma at the time when he came up with this.
33:25
The second person is actually probably more on my personal
33:29
side. It's a scientist from MIT called
33:32
Hargobind Khurana. So he won the Nobel Prize in
33:35
196869 for actually many different discoveries.
33:40
But but he was kind of the pioneer in oligonucleotide
33:44
synthesis. He built the first synthetic
33:45
gene. He actually was a professor at
33:49
my alma mater, Wisconsin, Madison as well, before he moved
33:52
to MIT. But he came from India.
33:55
He came to the the US via Canada as an immigrant set up shop, but
34:01
became kind of this pioneer in a, in a completely new space,
34:05
which is is impressive to me that, you know, in that
34:08
particular age where there wasn't that much instrumentation
34:12
or other things, like you really had to rely a lot on your
34:15
instincts to be able to predict which area is likely to going to
34:19
result in some of these novel discoveries.
34:21
So to me that was inspiring. And he's he was also a
34:25
Massachusetts local, but unfortunately passed away a
34:28
decade or so ago. The third person is actually
34:32
more current, but it's a space that I have really watched with
34:36
fascination is the whole CRISPR space.
34:38
And it's Jennifer Dowd and I'm talking about because to me, you
34:42
know, nowadays for people who are training in academia, even
34:47
like Crispr's taken for granted. When I first started in the
34:51
industry, CRISPR was not even in the in the armament of, of any
34:56
of the scientists, right? It was just an area that was
34:58
still being explored. But just how quickly that
35:00
particular technology has been able to really transform the way
35:04
everyone in biology does their research is just so impressive.
35:08
And that impact is what I, you know, dream.
35:12
So some of the things that I'm working on would eventually
35:14
happen. So to me, that's, that's really,
35:16
really inspiring and, and it's not only Jennifer, but also, you
35:20
know, so many people out there who work in the space have now
35:23
innovative even on that initial discovery that I think is just
35:27
going to translate to a lot of novel therapies for patients.
35:30
I'm so inspired by by by that original discovery.
35:34
So those are the three people. But again, as I said,
35:36
unfortunately, I guess I'm just a scientist at heart and that's
35:40
what I crave and enjoy everyday. So it's, it's exciting.
35:47
Yeah. But look, 3 great answers there
35:49
and thanks again for coming on and helping us out with this.
35:51
It sounds super exciting what you guys are doing in the pro.
35:55
I'm looking forward to seeing the next inflection points.
35:58
And and yeah, congratulations again on on Dustin, the first
36:01
patient and the fundraising round and look forward to to
36:04
following you guys and seeing the progress over the next few
36:06
years. So thanks again, Prakash.
36:08
It was a it was a pleasure. Thank you so much, Tom.
36:11
Really appreciate this opportunity and have a wonderful
36:14
rest of your day.